Iterative quality enhancement via residual-artifact learning networks for low-dose CT

Radiation exposure and the associated risk of cancer for patients in computed tomography (CT) scans have been major clinical concerns. The radiation exposure can be reduced effectively via lowering the x-ray tube current (mA). However, this strategy may lead to excessive noise and streak artifacts i...

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Veröffentlicht in:	Physics in medicine & biology 2018-10, Vol.63 (21), p.215004-215004
Hauptverfasser:	Wang, Yongbo, Liao, Yuting, Zhang, Yuanke, He, Ji, Li, Sui, Bian, Zhaoying, Zhang, Hao, Gao, Yuanyuan, Meng, Deyu, Zuo, Wangmeng, Zeng, Dong, Ma, Jianhua
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Brain Diseases - diagnostic imaging Brain Diseases - pathology cerebral perfusion CT deep learning dual energy CT Humans image restoration low dose CT Machine Learning Neural Networks (Computer) Plaque, Atherosclerotic - diagnostic imaging Plaque, Atherosclerotic - pathology Radiation Dosage Radiation Exposure Radiographic Image Interpretation, Computer-Assisted - methods Radionuclide Imaging statistical image reconstruction Tomography, X-Ray Computed - methods
Online-Zugang:	Volltext
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